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Abstract Mid-infrared (MIR) Kerr microcombs are of significant interest for portable dual-comb spectroscopy and precision molecular sensing due to strong molecular vibrational absorption in the MIR band. However, achieving a compact, octave-spanning MIR Kerr microcomb remains a challenge due to the lack of suitable MIR photonic materials for the core and cladding of integrated devices and appropriate MIR continuous-wave (CW) pump lasers. Here, we propose a novel slot concentric dual-ring (SCDR) microresonator based on an integrated chalcogenide glass chip, which offers excellent transmission performance and flexible dispersion engineering in the MIR band. This device achieves both phase-matching and group velocity matching in two separated anomalous dispersion regions, enabling phase-locked, two-color solitons in the MIR region with a commercial 2-μm CW laser as the pump source. Moreover, the spectral locking of the two-color soliton enhances pump wavelength selectivity, providing precise control over soliton dynamics. By leveraging the dispersion characteristics of the SCDR microresonator, we have demonstrated a multi-octave-spanning, two-color soliton microcomb, covering a spectral range from 1156.07 to 5054.95 nm (200 THz) at a −40 dB level, highlighting the versatility and broad applicability of our approach. And the proposed multi-octave MIR frequency comb is relevant for applications such as dual-comb spectroscopy and trace-gas sensing. Graphical Abstract

Abstract This study presented a surface-functionalized sensor probe using 3-aminopropyltriethoxysilane (APTES) self-assembled monolayers on a Kretschmann-configured plasmonic platform. The probe featured stacked nanocomposites of gold (via sputtering) and graphene quantum dots (GQD, via spin-coating) for highly sensitive and accurate uric acid (UA) detection within the physiological ranges. Characterization encompassed the field emission scanning electron microscopy for detailed imaging, energy-dispersive X-ray spectroscopy for elemental analysis, and Fourier transform infrared spectroscopy for molecular identification. Surface functionalization increased sensor sensitivity by 60.64%, achieving 0.0221 °/(mg/dL) for the gold-GQD probe and 0.035 5 °/(mg/dL) for the gold-APTES-GQD probe, with linear correlation coefficients of 0.8249 and 0.8509, respectively. The highest sensitivity was 0.070 6 °/(mg/dL), with a linear correlation coefficient of 0.993 and a low limit of detection of 0.2 mg/dL. Furthermore, binding affinity increased dramatically, with the Langmuir constants of 14.29 µM−1 for the gold-GQD probe and 0.000 1 µM−1 for the gold-APTES-GQD probe, representing a 142 900-fold increase. The probe demonstrated notable reproducibility and repeatability with relative standard deviations of 0.166% and 0.013%, respectively, and exceptional temporal stability of 99.66%. These findings represented a transformative leap in plasmonic UA sensors, characterized by enhanced precision, reliability, sensitivity, and increased surface binding capacity, synergistically fostering unprecedented practicality.

Abstract In the paper, an optical fiber sensor based on a seven-core fiber composite structure is presented, which enables dual-parameter sensing of bending and temperature. The proposed structure is fabricated by combining the strongly-coupled seven-core fibers (SC-SCFs) and a weakly-coupled seven-core fiber (WC-SCF). The SC-SCF acts as a beam coupler and enhances the Mach-Zehnder interference, while the WC-SCF serves as the enhanced section of another Mach-Zehnder interference. Therefore, the spectrum response of the fiber structure mentioned above exhibits a superposition effect of two Mach-Zehnder interferometers (MZIs). Among them, two dips corresponding to different MZIs are used to measure bending and temperature. The experimental results show the bending sensitivity and temperature sensitivity of the two MZIs are −4.238 nm/m−1, −2.263 nm/m−1, 0.047 nm/°C, and 0.064 nm/°C, respectively. It proves that our sensor is very sensitive to bending. Through the dual-wavelength matrix method, the bending and temperature can be measured simultaneously. With the benefit of the composite structure, low cost, and ease of fabrication, the proposed sensor can be used in harsh environments.

Abstract Understanding the morphology and function of large-scale cerebrovascular networks is crucial for studying brain health and disease. However, reconciling the demands for imaging on a broad scale with the precision of high-resolution volumetric microscopy has been a persistent challenge. In this study, we introduce Bessel beam optical coherence microscopy with an extended focus to capture the full cortical vascular hierarchy in mice over 1000 × 1000 × 360 μm3 field-of-view at capillary level resolution. The post-processing pipeline leverages a supervised deep learning approach for precise 3D segmentation of high-resolution angiograms, hence permitting reliable examination of microvascular structures at multiple spatial scales. Coupled with high-sensitivity Doppler optical coherence tomography, our method enables the computation of both axial and transverse blood velocity components as well as vessel-specific blood flow direction, facilitating a detailed assessment of morpho-functional characteristics across all vessel dimensions. Through graph-based analysis, we deliver insights into vascular connectivity, all the way from individual capillaries to broader network interactions, a task traditionally challenging for in vivo studies. The new imaging and analysis framework extends the frontiers of research into cerebrovascular function and neurovascular pathologies.

Abstract This paper demonstrates the novel approach of sub-micron-thick InGaAs broadband photodetectors (PDs) designed for high-resolution imaging from the visible to short-wavelength infrared (SWIR) spectrum. Conventional approaches encounter challenges such as low resolution and crosstalk issues caused by a thick absorption layer (AL). Therefore, we propose a guided-mode resonance (GMR) structure to enhance the quantum efficiency (QE) of the InGaAs PDs in the SWIR region with only sub-micron-thick AL. The TiOx/Au-based GMR structure compensates for the reduced AL thickness, achieving a remarkably high QE (>70%) from 400 to 1700 nm with only a 0.98 μm AL InGaAs PD (defined as 1 μm AL PD). This represents a reduction in thickness by at least 2.5 times compared to previous results while maintaining a high QE. Furthermore, the rapid transit time is highly expected to result in decreased electrical crosstalk. The effectiveness of the GMR structure is evident in its ability to sustain QE even with a reduced AL thickness, simultaneously enhancing the transit time. This breakthrough offers a viable solution for high-resolution and low-noise broadband image sensors.

Geospatial technology, including remote sensing, Geographic Information Systems (GIS), and Global Navigation Satellite Systems (GNSS), is identified as a key component in disaster management. It enables hazard, risk, and vulnerability mapping, aiding in the development of effective evacuation plans and resource allocation strategies. Moreover, it facilitates the evaluation of the Sustainable Development Goals (SDGs) and can contribute to improving accountability. While geospatial technology has seen rapid development primarily in developed countries, there's a growing need to expand its adoption globally, particularly in developing and vulnerable regions. Capacity building initiatives are highlighted as crucial in achieving this goal, emphasizing the importance of education, training, and infrastructure development. India is a leading example in the application of geospatial technology for disaster risk reduction, with significant integration into various phases of disaster management. The government's commitment and leadership, along with extensive training programs facilitated by institutions like Indian Space Research Organization, have been instrumental in promoting the widespread use of geospatial technology. Furthermore, India's efforts extend beyond its borders, with capacity building initiatives targeted towards the Asia-Pacific region through the Centre for Space Science and Technology Education in Asia and the Pacific (CSSTEAP). These initiatives aim to equip officials with the necessary skills to apply geospatial technology in disaster management within their respective countries. The paper underscores the importance of integrating geospatial technology into DRR strategies to build resilient communities and minimize the impact of disasters. It emphasizes the need for concerted efforts in capacity building to meet the growing demand for skilled professionals and promote evidence-based decision-making in disaster management.

Annually, a considerable quantity of forest is burned on a global scale. Therefore, it is essential to obtain precise and fast information regarding the size of burned regions in order to effectively monitor the adverse consequences of wildfires. The objective of this investigation is to indicate the effectiveness and usefulness of a deep learning (DL) architecture, such as Convolutional Neural Networks (CNNs), in the mapping of areas affected by fire, employing an eXplainable artificial intelligence (XAI) algorithm known as SHapley Additive exPlanations (SHAP) with accuracy evaluation criteria. Furthermore, this paper presents the evaluation of the Çanakkale-Kizilkeçili village wildfire. The research investigated the impacts of a variety of spectral indices, including the normalized burn index (NBR), differentiated normalized burn index (dNBR), Green-Red Vegetation Index (GRVI), simple ratio vegetation index (RVI), and normalized difference vegetation index (NDVI). At the end of the training process, the model achieved a training accuracy of approximately 0.99, with model loss values converging to approximately 0.1. The findings of the burned area identification analysis indicate that by incorporating spectral indices as supplementary information, the CNN model achieved a high level of accuracy, with an overall accuracy of 98.88% and a Kappa Coefficient of 0.98. Additionally, the SHAP technique was employed to gain insights into the output of the models. The feature importances of the spectral bands were determined through the SHAP analysis of the CNN model. Hence, the significance of the auxiliary data generated by the NBR, dNBR, and NDVI indices was identified as being the highest among the original bands and auxiliary data employed in this investigation.

Poplars (Populus sp.), a tree that grows rapidly species, are significant as industrial forest products. The delineation and monitoring of poplar cultivated areas are invaluable for decision-making processes. With the remote sensing technology, accurate detection of poplar planted areas could be determined much faster, more economically, and with minimum labor requirements. The objective of this research is to create a map of poplar plantations in the Sakarya region of Turkey utilizing Worldview-3 satellite imagery. Object-based image analysis (OBIA) through the application of the multi-resolution segmentation method (MRS) was employed to generate image segments, and then three prevailing machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF) and Rotation Forest (RotFor) were implemented to produce LULC maps of the study area including 11 landscape features. The most effective and contributing object features that assure high separability between landscape features were determined using a filter-based Chi-square algorithm for the prediction models constructed with SVM, RF, and RotFor classifiers. Results revealed that the SVM classifier achieved the highest overall accuracy (91.73%) with 38 features out of 88 features, about 3% improvement compared to the other algorithms. According to the SHAP analysis, the IHS feature was the most effective one in the constructed RF model, followed by the CI (red edge), NDVI-1 and NDVI-2 vegetation indices.

The paper investigates multidisciplinary education and specialization at the master's level, analyzes assessment methods, and proposes a technique for evaluating geospatial technology education. It emphasises is on pedagogical approaches, and highlights the importance of capacity building to meet the growing demand for skilled professionals in spatial information management. The research provides guidelines for improving course content, structure, and teaching methodologies, aiming to enhance the quality and relevance of geospatial education for both students and stakeholders. This paper is based on online data gathered from 115 universities across the globe, who are imparting geospatial education, campus-based and e-learning, also a survey was conducted of two thousand students. It supported in analysis and suggestion to develop a specific robust technique for assessment of the geospatial technology education and provides the corrective measures to the users of the technique. It proposed a framework aimed at enhancing the course content, organization, and teaching methods substantially. Further an attempt was made to adopt AHP model for investigating the statistical approach. It was aimed to know the actual values and significance of each parameter used for analysis of the study. In order to prepare a common guideline for improving the course structure, course content and teaching methodology, ANFIS architecture (Artificial Neural Fuzzy-Inference System) was adopted. Spatial information represented in the form of thematic maps in relation with the respective attribute data in Geographical Information System (GIS) environment is now used by all spectrum of the society, be it government organizations, industry, and academia. The result of the study has designed the criteria and common guidelines, which may be implemented, would able to help and curtail the challenges of the geospatial education as global vision for Local Action.

Lake Mainit is the fourth largest lake in the Philippines and water is one of the most important natural resources for humans and other living organisms. Sensors on satellites can measure the amount of solar radiation absorbed by surface water at different wavelengths, which can be compared with parameters of water quality. This study explored the use of Landsat 8 Operational Land Imager (OLI) and Geographic Information System (GIS) techniques for assessing the Phosphorus (P) content in the lake. Water quality data of Phosphorus from Environmental Management Bureau (EMB) was utilized to correlate with spectral values. Linear forward regression results yielded significant correlation with band combination B2/B4 with an R2 of 55% for Phosphorus (P). The model was then used to map the P content in the lake and was found out that the concentration ranges from 0.0228768 mg/L to 0.23145 mg/L. The study was effective in correlating P content and the bands of Landsat 8 OLI. In addition, GIS techniques were used in extracting spectral values of bands.

Despite the high susceptibility of the Philippines to natural disasters, there is a significant lack of human resources focused on disaster risk reduction (DRR). To address this, the University of the Cordilleras, in collaboration with the ERASMUS CBHE Plus Program: Geomatics in Disaster Risk Reduction of the European Union, has developed a curriculum for a GeoDRR program. This program is designed to produce professionals with expertise in disaster risk reduction, geomatics, and remote sensing. The curriculum development employed a mixed-methods approach using the ADDIE (Analyze, Design, Develop, Implement, Evaluate) Framework and the DACUM (Developing a Curriculum) method. The ADDIE Framework guided the structured development process, while the DACUM method identified the necessary competencies for students. The resulting UC MGeoDRR curriculum comprises 15 units of Core Courses, 15 units of Professional Courses, 9 units of Electives, and a 6-unit Capstone project. The curriculum is designed to be localized and contextually relevant, integrating DRR into research, innovation, and extension activities. The pilot program is recommended for graduates of related undergraduate programs and DRR professionals and practitioners.

This study investigates the national expansion of urban and artificial coverages in Chile between 2000 and 2022 using remote sensing techniques. The research employed Google Earth Engine and Landsat imagery to classify land use and land cover (LULC) changes. By analyzing annual cloud-free composites and employing random forest algorithms, the study produced detailed LULC maps, focusing on the 'infrastructure' class, which includes urban areas and major roads. Results indicate a significant 33% increase in infrastructure coverage, growing from approximately 380,000 hectares in 2000 to 510,000 hectares in 2022. This expansion was predominantly observed in seven regions, which accounted for 68% of the total infrastructure area and 83% of the national increase. A notable correlation (0.6) between urban growth and population growth was identified, suggesting that urban expansion is largely driven by demographic changes. However, regions with higher population growth exhibited more vertical urban development. The study highlights the complex interplay between urbanization, population dynamics, and regional planning, emphasizing the need for sustainable development strategies to manage urban growth effectively. Validation efforts showed high classification accuracies, with infrastructure class accuracies averaging 82% for the User and 87% for the Producer across the evaluated years.

This study focuses on the development of a people-centred early warning system (EWS) against floods in the Sirba River basin between Niger and Burkina Faso. This densely populated area has witnessed an increase in extreme flooding events in recent years. Several flood forecasting systems in the Sahel exist, although there is no EWS that integrates the four components of people-centred EWSs, namely risk knowledge, monitoring and warning service, dissemination and communication, and response capacity. The proposed EWS, named SLAPIS, includes a risk knowledge component that involves defining four levels of vigilance. Its monitoring and alert component involves a user-friendly web application containing real-time data collected through automatic stations. The EWS communicate seamlessly with the national alert system. The response capacity is strengthened through the creation of a flood zone atlas. In this framework, the EWS integrates significant geoinformatics in preparation of local risk reduction plans and the awareness of local communities. In the SLAPIS case study, multi-temporal classifications were conducted using Sentinel-2 data and high-resolution images (approximately 10 cm) generated through Structure from Motion (SfM) techniques. Digital Terrain Model (DTM) creation for hydraulic model calibration employed a multiscale approach, incorporating GNSS survey data processed via Precise Point Positioning (PPP), HydroSHEDS (approximately 100m resolution), and commercial 10m resolution data. All information was calibrated, harmonised, and integrated into the EWS model, which is accessible via a web platform. Capacity building encompassed direct training and field implementation to streamline the primary EWS generation steps.

The environmental repercussions of climate change triggered substantial alterations in existing ecosystem balances leading to a gradual reduction in biodiversity and the increasing degradation of soil and the built environment. This has a direct effect on agricultural production in particular, leading to increased crop water requirements, the spread of new pests and pathogens that are difficult to manage, and, thus, a general reduction in productivity. Through the analysis of multispectral data remotely sensed with latest generation of optical sensors (RGB, thermal, NIR and multi-spectral cameras), it is possible to recognize the health status and growth stages of crops. In fact, in relation to the amount of radiation reflected in the different bands it is possible to calculate spectral indices, or vegetation indices. The main vegetative indices are NDVI, SAVI, MSAVI2, GNDVI and NDRE, which are calculated by differentially combining the ultraviolet spectrum, the visible range, and the near and mid-infrared band.In this paper the outcomes of a multi-year monitoring of a citrus grove field near Rosolini (Syracuse), in south-eastern Sicily-Italy, are presented. This monitoring activity was conducted to highlight areas of productive greenery under stress, on which to act as a priority with specific agronomic interventions. In parallel with the direct monitoring phases of the citrus grove, the trend of average monthly and annual temperature and precipitation values over the past 24 years was also studied to more specifically contextualize the possible presence of significant variations in climatic trends and relate them to the results returned by the calculation of the indices.

Agriculture faces unprecedented challenges due to the increasing frequency and severity of natural disasters and extreme weather events. Agriculture sector needs resilience to support food production and livelihoods because it is susceptible to cyclones, floods, droughts, soil erosion, pests, and disease outbreaks. Geospatial technology plays a significant role in disaster management for agriculture, offering tools for preparedness, response, and recovery. This study explores the role of geospatial technology in early warning systems and risk assessment for different types of natural disasters that impact agriculture activity. Sustainable farming practices that are essential for resilience include crop diversity, climate-resilience varieties, and adaptation strategies. Capacity building and training are vital for effective geospatial technology utilization, especially in developing countries like India, where infrastructure and technology access may be limited. Tailored capacity-building programs are essential, emphasizing climate-smart practices, sustainable land management, and post-disaster recovery strategies. Access to financial services and insurance schemes enhances resilience by helping farmers cope with losses and recover from disasters. In order to improve resilience and sustainability in agriculture, geospatial decision support systems make it possible to evaluate alternative planning, optimise resource allocation, and implement adaptive management.

Geographic Information System Principles is one of the professional core courses for the students majoring in Geographic Information Science. It plays an important role in providing a theoretical foundation and technical support for subsequent professional courses. The course is offered in the second semester of the freshman year and is the first professional core course for students. There are four key issues that need to be addressed. Firstly, the main content of this course is extensive and the difficulty level is relatively high. Secondly,theory and software operation are disconnected in traditional classes. Thirdly, the traditional teaching mode can not well stimulate students' interest. Fourthly, the traditional teaching and learning evaluation method focuses on results but not process. Hence, we rely on the online teaching platform of our school to carry out Hybrid Instruction Mode, hoping to improve students' learning effect and improve teaching quality. Based on the outcome-based education (OBE) concept, we refine the teaching objectives, and subsequently enhance the teaching materials. Then, we have built a blended teaching mode. The whole teaching process is divided into four parts including "course guidance", "pre-class self-study", "in-class practice", and "post-class research". Online and offline teaching are connected with various activities. Discussions, exercises, reflections, assessments, critical thinking, paper reading and homework are online activities. Guidance, analysis and practice are offline activities. During the teaching process, we also reformed teaching method and carried out ecological civilization and scientific spirit education.We optimized the assessment method using the formative evaluation. The course objectives are almost achieved.

The study presented in this paper aims to contribute to the broader discourse on urban planning challenges by presenting a preliminary walkability assessment for the city of Zagreb, Croatia. Using the open-source OS-WALK-EU walkability assessment tool and open government and data from OSM, we demonstrated how data-based walkability assessment can provide information to support relevant policy making. The assessment considered two walkability user perspectives, one focused on enjoying free time and other on daily obligations (e.g. commuting to work). Preliminary findings indicate that walkability index scores are higher in the city centre and decrease towards the periphery for both user perspectives. Additionally, the results indicate that despite different user perspectives give different walkability scores, these are almost uniform across Zagreb, making all the assessed districts equally attractive for both, enjoying free time and do daily obligations.

Mangrove forests and seagrass meadows, collectively termed as blue carbon ecosystems (BCEs), play a pivotal role in carbon sequestration and climate mitigation. Blue carbon research enables advancing scientific knowledge and gaining insights into their current state, which is crucial for making informed decisions on its conservation and management practices. The Japan International Cooperation Agency (JICA) launched the “Comprehensive Assessment and Conservation of Blue Carbon (BC) Ecosystems and their Services in the Coral Triangle” or BlueCARES to pioneer joint research on blue carbon ecosystems and formulate conservation strategies at local and national levels. BlueCARES aims to establish a robust Blue Carbon Strategy and initiate the establishment of the Blue Carbon Network (BCnet), convening various stakeholders, including government agencies, local government units, academic institutions, and non-governmental organizations. BCnet organizes summits, workshops, and conferences to facilitate collaboration. Through capacity development initiatives, the project empowers stakeholders to actively engage in field surveys, conservation efforts, and the implementation of the Blue Carbon Strategy, even after the project concludes. This paper narrates the capacity-building engagements made by the Geomatics team, leveraging remote sensing and geospatial technologies to foster sustainable blue carbon management practices and ensure the long-term resilience of BCEs in the country.

Understanding the historical trajectory of land use and land cover (LULC) and predicting future alterations is essential to advancing the SDGs. Current researches on LULC in agriculture-dominated cities primarily focuses on the spatiotemporal pattern analysis of carbon storage changes, with relatively little attention given to LULC simulation studies. Moreover, most studies employing cellular automata (CA) models concentrate on urban centers, overlooking the specific sustainable development of agricultural cities. This paper selects Siracusa, Italy, as a case study, incorporating multiple drivers and constructing four development scenarios. The patch-generating land use simulation (PLUS) model is employed to predict the LULC for Siracusa by 2030 and to examine potential transformations under each scenario. Our findings show that 60% of the land in Siracusa is designated as cropland, predominantly situated in flat regions. Our simulation results reveal that urbanization and demographic growth are the main factors driving cropland conversion to urban areas. Different development scenarios exhibit significant variations in future land use structures; the cropland protection scenario emphasizes the stability and sustainability of agricultural development, whereas the economic development scenario highlights the substantial impact of urban and industrial expansion on agricultural land. These insights are instrumental for land use planning and policy-making in Siracusa and other agriculture-centric cities, providing a framework to guide urbanization while promoting agricultural sustainability.

In the digital age, open data has become a critical asset for fostering transparency, accountability, and civic engagement. However, the widespread availability of data has outpaced citizens' ability to effectively utilize it, underscoring the need for data literacy skills, especially among youth. This paper addresses the gap in data literacy by proposing an innovative teaching approach that leverages map-based storytelling to engage young people and enhance their data literacy. By integrating real-world open datasets into interactive visual narratives, this approach aims to captivate young audiences, develop critical thinking skills, and foster a deeper understanding of socio-economic issues. Through a case study of a Datathon project titled “Croatian Youth vs. EU: Who has it better?”, this research demonstrates the efficacy of combining data visualization and storytelling to promote data literacy and civic engagement. The findings highlight the potential of open data initiatives and open-source technologies in empowering youth and enhancing their participation in open government initiatives and policy-making processes.

Access to sanitation remains a major concern in the Philippines due to the increasing population and growing demand for basic needs. This study addresses poor sanitary conditions in slum areas, specifically in BaSECo Compound, Manila, where informal settlers rely on shared toilets. Utilizing GIS and MCDA, the study identifies areas suitable for community toilet facility (CTF) development. The Modified Huff Three-Step Floating Catchment Area (MH3SFCA) method quantified spatial accessibility to existing toilets. The SPAI map shows low accessibility in blocks 15 and New Site, with Gawad Kalinga, Habitat, and Old Site (N) having none. The Analytical Hierarchy Process (AHP) prioritized six criteria, with accessibility being the most significant (33.18%), followed by proximity to water supply (31.76%), communal septic tanks (14.53%), land use (8.2%), flood-prone areas (7.44%), and ground slope (4.89%). The final suitability map identified 14.12% of the 0.6 sq. km. area as highly suitable, including Block 15, Gawad Kalinga, Habitat, and Old Site (N). Ground truth verification visited five candidate sites, which were tested for improved SPAI maps. The inclusion of facilities in Block 15, GK 2, OSN, and H improved accessibility in northern BaSECo. Significant service area improvements for each CTF were observed using Voronoi polygons. This study demonstrates the effectiveness of integrating GIS and MCDA methods for CTF site selection.

To date, basic theoretical and methodological ideas of animated cartography have been formed, as well as general schemes for animated maps compilation. However, the technological issue remains relevant in this domain due to the abundance of modern tools that can be used for compilation of animated maps. Particularly, most of currently implemented cartographic animation methodologies require the use of a variety of specialized software, including geographic information systems (GISs), vector and raster graphical editors, video effects editing software, and others. So the data processing and conversion techniques are extremely complicated in some cased, when data converted from one software platform to another.In our study, we established investigation of popular desktop GISs functionality application as a full-cycle animated cartography environment. In such a context, we discover approaches to improve methodology of animated maps automated compilation. To prototype a full-cycle animation in GIS environment, we developed a set of animation algorithms implemented in QGIS and documented the algorithms using UML (Unified Modeling Language) diagrams.

In the current paper, we discover a case study of medical service areas zoning automation needed to ensure effective operation of phthisiatric service. The study was conducted for the administrative area of Saint Petersburg city (Russia). Originally, the process of phthisiatric service areas zoning bases upon outdated interpretation of medical maintenance of territories, and assumes splitting of living buildings list compiled for some administrative territory. This leads to appearing of different zoning features (service area geometry and topology, workload imbalance, etc.), which impact the quality and effectiveness of medical service. No unified and(or) open source tools for geographically corrected (in the meaning of accounting of the different spatial factors and variables) medical service areas zoning are available now. Our study is focussed onto closing of this lack basing on geospatial techniques and data management methods. To ensure the automated phthisiological medical service areas zoning we elaborated and tested a set of scripts available to be running in QGIS. Elaborated methodology was documented and considered as a possible for implementation into technological chain of Saint Petersburg phthisiatric service.

Geospatial technology involves the use of maps, satellite imagery, and spatial databases to collect, analyze, and visualize geographic data. Understanding the link between blockchain and geospatial technology is crucial as it opens the door to innovation and progress in both fields. “The use of Blockchain as a system for managing and verifying geospatial data necessitates accuracy, integrity, and trustworthiness”(Pengxiang et al 2022). Improving data integrity, promoting collaboration, and revolutionizing applications in the geospatial domain are all possible through the combination of these two technologies. The World Wide Web (WWW) has expanded connections worldwide, but it also makes networks susceptible to multiple attacks from various anonymous sources. Packets of fixed or variable sizes are used for transmitting data across nodes. Data is encrypted using secure algorithms at the application level, then packetized and sent at lower levels in the OSI architecture. The encrypted data can be obtained by an intruder by organizing the data contents of each packet once they have access to all the packets. Intruders may also attempt to break the secured algorithm used by the sender. This sophisticated approach to Data Security is the content and order of data, ensuring that unauthorized individuals are unable to discern sensitive information during data transmission. The primary objective of this paper is to introduce a novel spatial encryption technique designed to enhance the security of data transfers within networks. This paper, founded on a comprehensive literature review, endeavors to propose a research project focused on exploring futuristic solutions, particularly within the post-quantum era.

Soil erosion is a serious environmental concern in Tagoloan River Basin (TRB), a major watershed in Northern Mindanao, Philippines. It leads to soil loss causing detrimental impacts such as decreased soil productivity, nutrient loss, siltation, and water quality degradation among others. These impacts are better understood by estimating the degree of soil loss in the watershed and visualized in a GIS-based and factor-based approach using the Revised Universal Soil Loss Equation (RUSLE) model. Thematic maps of soil loss were generated with factors for rainfall erosivity (R), soil erodibility (K), topographic (LS) consisting of slope length (L) and slope steepness (S), cover management (C), and support practice (P). Factors were calculated separately and multiplied together to develop combined soil loss maps. Results show that TRB has an actual mean annual soil loss of 153.20 tons/hectare/year with 47.15% of the study area having very high to very severe susceptibility to soil loss. Further, a comparison of the potential (RKLS) and actual (RKLSCP) soil loss maps indicates the significance of cover management and support practices to the resulting mean annual soil loss. The present characterized soil loss level maps and its understanding driving forces of soil erosion for the planning of management practices and mitigating environmental hazards in the watershed.

The issue of flooding in the town of Jasaan in Misamis Oriental province poses a threat to 42% of the population. This is more worrisome during the rainy season, especially in the months of October and December were typhoons are at their strongest and passes in proximity to Mindanao island. Residents have reported significant damage to their properties due to flooding, and typhoons like Super Typhoon Odette in December 2021 have worsened the situation when it brought rain while the dike system was dilapedated. The dike systems in the area have been damaged by these natural disasters, and the lack of repair has made the situation worse. Flood hazard mitigation measures have since been implemented. With the frequent occurrence of flooding in the Cabulig River, a validated model is needed to investigate the behavior of the river during strong and significant rainfall events while taking into consideration the current state of the existing dike systems by coupling the modelling systems HEC-HMS and HEC-RAS. Data from the USGS, DOST-PAGASA, DPWH, and Landsat 8 have been used in this study. The results of the Flood Inundation Analysis showed that most of the flooded areas in Barangay Lower Jasaan and Bobontugan are urban areas which exacerbates during heavy rain events. The results of the simulations of the researchers' proposed dike structures shows that it should be extended towards the upstream direction and a minimum dike height of 2.5m is needed with no discontinuities. A 2.5m high dike on the Bobontugan side should also be placed.

Tropical cyclones and typhoons pose significant threats to coastal ecosystems, particularly mangrove forests. Assessing the extent of mangrove damage and monitoring recovery post-disaster is vital for effective conservation and management strategies. Space-based observations have become increasingly common in monitoring forest damage in tropical regions. However, the predominant methods for change detection rely on satellite optical imagery, which faces challenges in humid tropical zones. Technological advancements have led to the development of detection techniques utilizing synthetic aperture radar (SAR) to address cloud cover issues. The study specifically targets Siargao Island Protected Landscape and Seascape (SIPLAS), the largest contiguous forest in the Philippines, from destruction caused by Super typhoon Rai (Odette). The study utilized SAR time-series data, unsupervised log ratio change detection method and Cumulative Sum (CuSum) analysis via Google Earth Engine (GEE). Analysis of Sentinel-1 GRD images found VV polarization had the highest accuracy at 92% and an F-measure of 0.92. VH polarization had 87.5% accuracy and an F-measure of 0.87, while combining VV and VH and VV/VH achieved the same results of 80.5% overall accuracy with an F-measure of 0.82. With that, results indicated that 53% of mangroves in SIPLAS area were damaged, with 38.05% regenerating by 2022 and 56.75% by 2023, notably after September 16, 2022 detected from CuSum analysis using the VV polarization. These findings substantiate the robustness of the data and methodologies employed in monitoring mangrove resilience and supporting conservation initiatives within the SIPLAS area.

The north eastern region of India is characterized by limited physical connectivity and challenging terrain, that requires innovative approaches for effective developmental planning and monitoring using geospatial data and user-centric applications. This paper focuses on leveraging space-based data and applications to address these challenges and enhance decision-making processes in governance. A unified framework for interoperable spatial data sharing has been developed, ensuring that data is easily discoverable, searchable, and consumable through metadata standards and OGC interoperable data services. A dedicated spatial data repository has been established, offering real-time access to over 1400 geospatial datasets and facilitating better decision-making and geo-analytics among diverse user groups. The repository operates on an open-source platform with integrated map viewers and geo-analytics tools, allowing for interactive exploration and visualization of geospatial data. Additionally, 35 user-centric geospatial applications have been operationalized, all developed using open-source geospatial tools and standards. Capacity building programs have been conducted to increase awareness and maximize the utilization of geospatial data services and applications across the region. The platform has boosted utilization of geospatial data services and applications in the region for overall planning and developmental activities for smart governance and societal benefits.

Disseminating up-to-date information on dengue mosquito habitats aided by modern GIS approach would yield benefits to people being informed on the localized developments of the vector and therefore reduce people’s risk of being affected by the disease. This paper discusses the development of this monitoring and sensing mechanism through a Web-based GIS mapping and visualization. It utilizes the environmental, WHO dengue indices and weather information for a case of a city in the Philippines to visualize hotspot areas for habitats of dengue vectors. The paper would aid the government in deploying key assets and supporting DOH’s 5S drive or Search, Self, Seek, Support and Sustain strategy to curtail the spread of the disease. Through this paper, the author intends to develop a localized web-based platform Aedes mosquito vector. Presented in this study provides the data workflow from field observations and the inputs from environmental and meteorological data which has the potential to help enhance the data dissemination as well as the IEC campaign of local health leaders in reducing the mortality and morbidity of people affected by Dengue mosquitoes. This visualization platform includes an easy to set-up system using the available Web-based GIS which can be updated depending on the availability of field data and meteorological observations.

Tree canopy plays an essential role in the biophysical activities in forest environment. During the past two decades, individual tree delineation using high-resolution imagery data has become a hot topic in forest sensing research. Individual Tree Crown (ITC) segmentation methods aim to generate masks that delineating the boundary of each ITC, which supports various tree parameter extractions. Thanks to the rapid development of deep learning, the ITC segmentation methods achieved remarkable improvement. However, existing research suffers from the limited availability of the datasets, and the lack of evaluation standards as well as task-orientated neural networks. In 2024, the International Society of Photogrammetry and Remote Sensing (ISPRS) launched the first International Contest of ITC Segmentation. The contest aims to reveal the state-of-the-art of the ITC method development using high-resolution images, to clarify the remaining barriers and challenges, and to guide further explorations in the field. This paper overviews the contest and reports the initial finding regarding the impact factors of the method performance.

Land cover classification is critical in various fields, including environmental monitoring, urban planning, and ecological assessment, facilitating informed decision-making processes. Traditional land cover classification methods often involve labor-intensive and time-consuming processes, relying on manual intervention and predefined algorithms. The emergence of deep learning techniques, particularly convolutional neural networks (CNNs), offers a promising solution to automate this process, albeit with complexities in implementation. This study addresses the limitations of existing Geographic Information System (GIS) software and plugins by proposing a novel approach utilizing the Pix2Pix architecture, a type of CNN, for automated land cover classification. The proposed Land Classification Plugin (LCP) integrates seamlessly with QGIS, offering an end-to-end solution for generating classified static maps. The methodology involves preprocessing data, utilizing the Pix2Pix model for image segmentation, and post-processing to produce georeferenced outputs. The development of the LCP involved extensive software and hardware configurations, including essential components like GDAL/OGR, PyTorch, and OpenCV. The plugin's architecture comprises a user-friendly interface for region selection, clipping, and classification aided by the Pix2Pix model. A layout manager feature also allows for the creation of composite maps for enhanced visualization. The accuracy assessment of the LCP demonstrated an overall accuracy of 83.40% across diverse land cover classes, indicating its efficacy in classification tasks. The plugin's capabilities offer significant potential for applications in land management, environmental surveillance, and urban planning, revolutionizing current practices in land cover classification within the realm of GIS software.

Coastal blue carbon ecosystems, such as mangrove forests and seagrass meadows, are important for various environmental functions, serving as habitats, food sources, and carbon sinks. Despite their importance, seagrass ecosystems are experiencing global decline due to numerous stressors, including anthropogenic activities. To address this, effective monitoring and management strategies are crucial. Traditional in situ surveys for seagrass monitoring are labor-intensive and time-consuming. Alternatively, remote sensing technologies, particularly satellite imagery, offer a more efficient means of mapping seagrass distribution over large areas. However, accurately assessing seagrass cover at a local scale remains challenging with satellite data alone. Recently, unmanned aerial system (UAS) technology has emerged as a promising approach for seagrass mapping. With its fine resolution and flexibility for image acquisition, UAS can provide more precise estimates of seagrass cover, complementing satellite data. This integration enhances accuracy assessments and expands data availability for training and validation purposes. Under the BlueCARES Project, a drone training program was developed to equip stakeholders with the skills needed for cost-effective UAS-based seagrass mapping. The drone training program encompasses lectures on UAS basics, flight planning, and image processing. Beyond training, collected data are used to create seagrass maps and assess seagrass density. This initiative not only advances seagrass conservation but also fosters collaboration between government agencies, academia, and local communities to achieve sustainable management of blue carbon ecosystems.

The generation of 3D spatial datasets, especially in indoor space, has continually been a challenge in developing spatial applications that are aimed for the realistic representation of the real world. Despite their potential, the creation of 3D spatial data remains a challenge, particularly in terms of cost and complexity associated with traditional methods. While this method proves to be a reliable and accurate method of creating such data, this approach entails challenges in economic, computational, and human resource aspects. Alternatively, omnidirectional images have emerged as cost-effective alternatives for representing 3D spaces, offering a comprehensive field of view and facilitating the generation of structure and appearance representations through mesh-based 3D maps. This study evaluates the geometric accuracy of a cloud-based image-based mapping platform for generating 3D representations of indoor spaces, aiming to assess its suitability as an alternative to desktop-based platforms. We obtained 30 sample lines and compare respective measurements from each of the generated meshes to a ground truth value. Results show that while there are no significant differences in the mean errors, the mesh generated from a cloud-based platform produced minimal errors. This demonstrates the potential of this platform for generating 3D representations of indoor space, acceptable in both geometric accuracy and visualization capabilities.

Geospatial technologies present a significant opportunity to assess solar photovoltaic (PV) power potential through remote sensing techniques and geospatial analysis. This aligns with the goal of ensuring access to affordable, reliable, and sustainable energy, one of the Sustainable Development Goals (SDG) that the Philippine National Economic and Development Authority (NEDA) aims to achieve. In support of the said SDG, a research project, called Project SINAG, has developed a method for estimating solar PV power resources utilizing high-temporal remotely-sensed data. The primary objectives of this study are twofold: first, to develop a webGIS platform for solar energy resource information access and decision support; second, to conduct information dissemination and capacitybuilding activities for various stakeholders in the solar energy sector. The localized assessment of solar PV power potential incorporates meteorological and geomorphological factors such as high temperature, dust deposition, and slope. Collaboration with academic institutions, government agencies, and the solar energy industry has been instrumental in providing capacity-building through training workshops, internships, and on-the-job training. Moreover, the project has produced information, education, and communication (IEC) materials, launched a webGIS portal, and supported data requests and student research. The webGIS platform serves as the primary information dissemination tool for the project, enabling rapid sharing of research outputs with end-users. To enhance the platform, future developments may include additional functionalities such as community forums, client feedback mechanisms, mobile application development, and the integration of real-time data for map layers and forecasting tools.

Immersive technologies are becoming a powerful tool for educators across multiple disciplines including geospatial science. They offer new ways to engage and educate geospatial students, removing barriers that may exist in traditional teaching methods. Especially because the limitations of 2D screens are often exceeded by the complexity of modern data sets. Examples of challenges that educators often encounter are explaining theoretical concepts in the classroom, providing alternate scenarios, preparing students for physical labs, and limited / restricted access to physical sites. Immersive technologies can be a great resource to support on-site lectures and enhance remote learning, a necessity in today's educational panorama. Immersive technologies typically include virtual, augmented, and mixed reality. Each method offers different pedagogical advantages and poses different challenges. Before any implementation, these advantages and challenges must be examined and understood to maximize the benefit of immersive methods to the students and mitigate potential drawbacks that could hinder learning outcomes. This paper provides a review of the different immersive methods, presenting examples of their application in geospatial education with lessons learned and recommendations for future work. The case examples include a variety of different instruments and tasks such as the simulation of GNSS, differential leveling, total station operations, and airborne LiDAR data collection.

The accelerated adoption of photovoltaic (PV) systems for renewable power generation in the Philippines will yield a tremendous amount of PV waste. However, the country lacks a comprehensive framework for understanding PV module waste volume growth and optimally siting Treatment, Storage, and Disposal (TSD) facilities for PV waste management based on minimum siting factor. The study aims to develop a regional framework that utilizes a geographic information system (GIS)-based approach to find the potential sites and optimal locations of TSD facilities in Laguna, Philippines, based on the estimated PV module waste growth of solar energy systems with service contracts and minimum siting criteria under Department of Environment and Natural Resources (DENR) Administrative Order 2013-22. By 2060, the projected amount of PV module waste in Laguna province will be around 162,230 metric tons based on the waste projection results. The suitable sites cover around 19,720.5 hectares or 11.21% of the total land area of Laguna, which were determined by various siting factors that encompass social, economic, and environmental factors and obtained using binary suitability analysis and vector overlay analysis. The optimal locations of the two TSD facilities were identified using strategic positioning based on hotspot analysis, clustering algorithm, and proximity analysis. Consequently, the distance and GHG emissions of the two optimal locations were calculated. Indeed, the approach will serve as a guide for policymakers and other stakeholders in understanding PV module waste growth and strategically and optimally position TSD facilities on suitable sites for efficient and effective PV waste management.

The north eastern region (NER) of India is among the hotspot regions for thunderstorm events leading to substantial loss of lives and properties every year. A reliable thunderstorm early warning system can prevent these losses. As these events are highly dynamic, prediction becomes equally challenging. Diagnostic studies of such events could provide more understanding of lightning and storms over a region which could then be used as input for the prediction of such events. Improving the efficiency of the prediction of associated meteorological parameters could improve the prediction of thunderstorm. In this study, two thunderstorm events have been identified with high lightning flash counts, using observations from the INSAT-3D satellite and lightning detection sensors. These observations revealed that one of the events was caused by a cloud system that originated elsewhere and travelled over the area of impact while the other event was an isolated convective system that occurred over a small region. The characteristic features of thunderstorms, like vertical wind velocity, cloud ice water content, relative humidity, cloud fraction and radar reflectivity, etc. were simulated using the WRF model. These simulations were validated with the ERA-5 reanalysis data and observations from Radar and satellite data. Validation results suggest that the WRF model could predict these features associated with thunderstorm events quite reliably. The study helped to improve the severe weather warning over NER of India and the stakeholders are trained on effective utilization of such warning.

This paper describes the discipline of geodesy and geoinformatics with a focus on university studies and career opportunities. It is shown how the disciplines contributes to the solutions of major societal challenges such as sustainable development, mobility and global change. Then, the structure of German universities is shortly explained, followed by an overview of where in the country the discipline can be studied. Finally, the example of Leibniz University Hannover, one of the prime centres of excellence in the field, is described in more detail. We thus hope to encourage young talents to consider this exciting field of studies for their future career.

The digitization of maps poses a significant challenge for GIS operators and engineers, particularly in encoding bearings and distances into GIS software. This study, titled “Circinus: An AI-Based Technical Description Plotting,” represents a substantial advancement in the digitization process of land records. Leveraging artificial intelligence, this application enhances the efficiency of digitizing land survey maps, potentially surpassing the laborious manual input required for geographic information. By expediting the encoding process of scanned maps, the application enhances the accessibility and availability of digital land records. This advancement holds considerable benefits for effective land asset planning and improved financial operations, particularly benefiting prospective homebuyers and landowners. The tool offers streamlined encoding through its semi-automated features, facilitating ease of use. Additionally, its optimization features have the potential to significantly enhance the productivity of engineers and GIS operators involved in encoding scanned land survey maps.

Microplastic, resulting from the breakdown of larger plastic objects, poses substantial threats to ecosystems and human health. This research addresses the escalating environmental concern of airborne microplastics by developing a low-cost detection machine employing infrared sensors. The near-infrared light is attached to the machine for detection using the sensors. Different types of microplastic, specifically less than 5mm, are used. There are 3 types of plastics used for detection, 30 pieces each of polyethylene, polystyrene, and polyester microplastics. Each type of microplastic is tested for detection in different time frames, 1, 2, 3, 4, and 5 minutes each, and the three types of plastics are combined randomly for the last detection with the same number of minutes. Because of the final results, it can determine the effectiveness of the sensor in detecting airborne microplastics in terms of the number of correctly detected microplastics. In conclusion, this research advances our capacity to detect airborne microplastics, contributing to environmental conservation and public health protection. The detection machine's development and the insights gained from the study have broader implications for standardizing methodologies, advancing technology, and fostering collaborative efforts in addressing the challenges posed by airborne microplastics.

Most of the global population have shifted to urbanization with advancements in technology. With this transition comes the responsibility of applying these technologies to promote sustainable development practice. Since energy demand is highest in the urbanized areas, it is important that proper assessment and management of energy resource is considered in policy-making and urban planning. This study investigated the estimation of cooling energy demand in Iloilo City Proper, Philippines using a 3D city model integrated with building attributes including building functions and year of construction, while also taking into consideration meteorological factors in the area. The proposed method used the application SimStadt2.0, following the German computation standard DINV18599. Building functions and year of construction were extracted from available building attributes data and satellite images respectively, using the free and open-source software QGIS. A CityGML Level of Detail 1 was generated from 3Dfier using building footprints and LiDAR point cloud data, along with the extracted building attributes of the 5,426 buildings. Meteorological data from INSEL 8 were also considered in the estimation of cooling energy demand in SimStadt2.0. Results showed a monthly energy demand of 12.33 kWh to 313,530.08 kWh in the study area. The estimated energy demand values were higher than the standard mean for different building functions in the country, but within the expected range of values for each season. Urban Heat Islands (UHIs), analyzed using Land Surface Temperature (LST) values, also have significant correlation to areas with higher cooling energy demands. However, inconsistencies can imply the need for further investigation.

In this study, we extracted above-ground biomass (AGB) information for forests in different ecological zones of ASEAN (Association of Southeast Asian Nations) by integrating freely-available global AGB, forest, and ecological zone map products. Our objective was to assess the suitability of the data and proposed approach for national reporting of forest carbon stocks. We compared the satellite-derived AGB values of forests in ASEAN countries with the corresponding AGB values provided by the Intergovernmental Panel for Climate Change (IPCC) in their Guidelines for National Greenhouse Gas Inventories (which are derived from ground-based measurements of forest AGB). For this, we used a map integration approach that ensures that AGB data is extracted from areas corresponding to the Food and Agricultural Association (FAO) of the UN’s definitions of “forest” and “ecological zones”, as recommended by the relevant IPCC Guidelines. We found that the average satellite-derived AGB values extracted for each ecological zone were generally lower than the values for natural forests (but higher than the values for plantation forests) provided in the IPCC Guidelines. Further investigation showed that this was partly due to the presence of many erroneously low AGB values for forests in the extracted results, caused by errors in the data masks applied to the original global AGB map, including masks of cropland, urban areas, bare soil, and water bodies. Our findings suggest that further processing is necessary before using satellite-derived data for national reporting of forest carbon stocks in ASEAN countries, and we give a few possible options for this.

Shoreline changes are a critical indicator of coastal vulnerability, impacted by natural processes and human-induced factors such as climate change. This study investigates shoreline changes in New Washington, Aklan, Philippines, focusing on erosion and accretion patterns since 1985. With the use of satellite imagery, coupled with Remote Sensing and Geographical Information System (GIS) techniques, this study examines the spatio-temporal variability in erosion and accretion patterns. Through the Digital Shoreline Analysis System (DSAS), a quantitative analysis of shoreline change rates was conducted. Using four (4) DSAS change metrics such as Endpoint rate (EPR), Linear Regression Rate (LRR), Shoreline Change Envelope (SCE), and Net Movement Shoreline (NMS), it was identified that the shoreline change in Bakhawan Park area is due to its location being at the mouth of Aklan River. Most of New Washington’s shoreline experiences erosion with 0-1.46 m/yr from LRR, eroding at most 40m of sediments in thirty years; evident along areas of Brgy. Tambac and Poblacion. The largest shoreline deviation can be observed in the areas located near the mouth of Batan Bay with 153 meters since 1985.

Remote Sensing is proven to be helpful in various ways, and for an agricultural country like the Philippines, mapping in farmlands is not that common. Using the Sentinel 1 Synthetic Aperture Radar (SAR) data, the study reveals rice paddy classification brought by supervised machine learning on the municipality of Guimba, in Nueva Ecija - the Rice Bowl Capital of the country. The study in Guimba, Nueva Ecija, addresses the absence of comprehensive rice classification studies, focusing on creating a classified rice paddy map and assessing spectral indices (NDMI, NDVI, NDWI) using synthetic aperture radar and optical imager. Results show successful differentiation between rice and non-rice paddies. Temporal analysis emphasizes monitoring water availability, soil moisture, and vegetation health. Despite signs of overprediction in the CART model, its effectiveness in mapping rice paddies is notable. The spatial distribution maps contribute to targeted monitoring, enabling efficient interventions and improved agricultural practices. This research highlights the inherent use of remote sensing in rice crop management, offering valuable insights for farmers and country's food security.

A recent study has been conducted to evaluate the current state of green spaces in Pasig City, Philippines, and to propose a Climate Resilient and Localized Urban Greening Plan (CRLUGP) to address urban greening that is essential for creating sustainable cities, especially in the face of climate change and urban population growth. The study utilized Geographic Information Systems (GIS), Remote Sensing (RS), and Machine Learning (ML) to map green spaces, analyze climate risks, and identify necessary corrective measures. The findings revealed that Pasig City currently only has 70.18 hectares of green space, which is significantly below the World Health Organization's recommended minimum of nine (9) square meters of green space per capita. Projections indicate that by 2032, the city will need 925.72 hectares of green space to meet the needs of its growing population. Additionally, the study identified a connection between low canopy cover and increased urban heat island effects, with the top 10 barangays showing a canopy cover of only 27.64%, well below the recommended 40%. Moreover, areas with limited green spaces were found to be at a higher risk of flooding, highlighting the importance of more vegetation for managing stormwater and mitigating climate-related risks. The study recommends a strategic approach to urban greening, leveraging spatial analysis for continuous monitoring and evaluation. By integrating spatial technologies and data-driven decision-making, Pasig City aims to establish a more sustainable and resilient urban environment for its residents.

Vietnam secures a place among the top countries that possess the largest mangrove areas worldwide. The mangrove forests are mainly found in the Northern, Northeast, Central and Southern Delta, providing goods to habitants, and make significant help mitigate global climate change. Despite this, mangroves are severely threatened because of extensive deforestation in Vietnam. Recent advances have utilized remotely sensed imagery to present the spatial and temporal distribution of mangroves. Nevertheless, the approach is limited by imagery availability and computing resources, and difficult to share within the community. Therefore, a shareable Web-based tool that reinforces coastal managers to monitor the changes in mangroves is needed. Recently, Google Earth Engine (GEE) is a cloud-based geospatial analysis platform, which allows users to freely exploit the availability of satellite imagery and harness their computing capacity. This research aims to use GEE to detect mangrove changes, a case study in Tien Yen district, Quang Ninh, over a period from 2010 to 2020. Four supervised classification algorithms, including Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes classifier, and Classification and Regression Trees (CART) have been implemented on GEE platform to select the best algorithm to produce spatial-temporal mangrove maps, then change detection of mangroves is performed. The results showed that RF demonstrated the highest accuracy with overall accuracy and kappa values of 91.04% and 0.75 respectively. The mangrove areas statistically reported by the GEE-based platform are in line with the governmental statistic report annually, marking the capabilities of GEE in natural resource management.

Like in most bachelor engineering programs, the education of geomatics students typically begins with an extensive theoretical introduction covering fundamental subjects. The general objective of the bachelor program is to cultivate geomatics engineers with robust hands-on knowledge. Nevertheless, the progression to a stage where students can independently engage in exercises with minimal supervision is a long process. Although field work-based practices are dominant in this field, they often entail activities limited to testing, experimenting, and the application of basic theories. While this approach is efficient in imparting routine knowledge, it may lead to the fragmentation of acquired knowledge, thereby lacking a comprehensive understanding of the broader significance and context. Students often encounter more complex challenges when commencing work on their bachelor thesis, leading to a steep learning curve within a constrained timeframe. This late exposure to real-world tasks can be a limiting factor, hindering a smoother transition from education to professional work. This paper provides a case study of project-based learning (PjBL) within a geomatics bachelor program. Specifically, it discusses a digital heritage preservation project where students documented an old Norwegian log house throughout the autumn semester of 2023. The outcomes of this learning approach are analyzed with the help of the key features of the PjBL methodology, focusing on students' learning outcomes and their assessment measures.

This paper presents a collaborative effort to develop localized solar photovoltaic (PV) power output (PPV) forecasting models for the Philippines using geospatial data. It underlines the importance of solar energy in the country and discusses the opportunities and challenges associated with PPV forecasting. Project SINAG, a two-year research project, aimed to develop solar PV output forecasting models through a collaborative approach with academic institutions, solar energy industries, and government agencies. Actual PPV data from 43 solar PV installations were analyzed alongside meteorological data from the PAGASA weather bureau, ERA5, AHI-8, and FY- 4A. These datasets were filtered based on a one-year period to ensure quality. The study employed SARIMAX, LSTM, and XGBoost models individually and in hybrid models to develop the forecasting models. Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In a case study in Baguio City, the SARIMAX model exhibited strong seasonal dependence, providing more accurate forecasts in dry seasons than in wet seasons. Additionally, the forecasting accuracy of each model (SARIMAX, LSTM, and XGBoost) varied based on the month and location of the installation, emphasizing the need for local and season-based PPV forecasting models. Despite implementation challenges, such as collaboration arrangements, bureaucratic barriers, and budget constraints, the project produced thirteen research publications and provided data for three student theses. This paper also demonstrated diverse engagements and contributions that emphasize the significance of collaborative research in conducting nationwide-scale data-driven projects.

Earth observation (EO) applications offer significant potential for addressing challenges and driving sustainable development in the Philippines. Despite the positive effect EO services can bring, its utilization encounter barriers that includes the lack of awareness of its benefits. The Philippine Space Agency (PhilSA) initiated the Space Information Infrastructure Capacity Building and Training Program (SIICaP) that aims to promote awareness of EO applications through free and inclusive online webinar platforms. This paper highlights the various webinars conducted by SIICaP showing their relevance in raising awareness of EO data, services, and programs. The analysis of evaluation results from the webinar participants further illustrates the program’s reach and success. By showcasing SIICaP, this paper contributes to efforts in developing capacity building programs that aim to have the same objectives of SIICaP.

Air quality management is crucial in the Philippines due to its profound implications for public health, ecosystems, and sustainable development. Project AiRMoVE, short for the Ambient Air Remote Sensing, Modeling, and Visualization Environment project, aims to enhance air quality management through engineering solutions and geospatial technologies such as remote sensing (RS), geographic information systems (GIS) and numerical modeling. Implemented by the University of the Philippines Training Center for Applied Geodesy and Photogrammetry (UP TCAGP), Project AiRMoVE collaborates with key stakeholders to identify attainment and non-attainment areas in the National Capital Region (NCR) for air quality monitoring. This paper provides an overview of Project AiRMoVE's objectives, methodologies, and outcomes, emphasizing the significance of RS and GIS in revolutionizing air quality monitoring and management. It underscores a systematic approach adopted in enhancing government capacity for air quality management -- a combination of workshops, technical trainings, and stakeholders' forums.